Update README.md

README.md

@@ -25,6 +25,47 @@ SnakModel comes as an instruction-tuned, and a base version. In addition, each m
 
 Text only, with instructions following the `[INST] {instruction} [/INST]` template.
 
+Quickstart:
+
+Here is a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.
+
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "NLPnorth/snakmodel-7b-instruct"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Hvor ligger IT Universitet?"
+messages = [
+    {"role": "system", "content": "Du er Snakmodel, skabt af IT-Universitetet i København. Du er en hjælpsom assistent."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=20
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
 **Output**
 
 Text only.